EXPOLIS simulation model: PM2.5 application and comparison with measurements in Helsinki

Exposure to airborne particulate matter in working from office and working from home employees

The main aim of this study is to quantitatively evaluate the differences, in terms of exposure to PM (particulate matter), between WFO (working-from-office) and WFH (working-from-home) conditions. Two measurement surveys were performed: a long-term and a short-term campaign, focused on the monitoring of personal exposure to size-fractionated PM in these different working conditions. Results of the long-term campaign show that the WFH subject is exposed to higher (up to 4 times) PM concentration, compared to the WFO subject. Specific activities performed by the subjects impacted their exposure concentrations, even if the most relevant contribution to total exposure was made by desk work. Results of the short-term campaign indicate that the subjects can be divided into two groups: subjects most exposed during the WFH mode (HE_H - Higher_Exposure_Home) and subjects most exposed during the WFO mode (HE_O - Higher_Exposure_Office). HE_H group is exposed to levels of pollutants up to 4 times higher in the domestic than in the office environment, during the moment of desk work. The HE_O group is exposed to higher (double) concentration levels during desk work during the WFO day. Considering the possible growing trend towards remote work it is important to evaluate these "new domestic offices" comprehensively.

A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Outdoor concentrations of particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) are often used as a surrogate for population exposure to PM_2.5 in epidemiological studies. However, people spend most of their daily activities indoors; therefore, the relationship between indoor and outdoor PM_2.5 concentrations should be considered in the estimation of population exposure to PM_2.5. In this study, a population exposure model was developed to predict seasonal population exposure to PM_2.5 in Seoul, Korea. The input data for the population exposure model comprised 3984 time-location patterns, outdoor PM_2.5 concentrations, and the microenvironment-to-outdoor PM_2.5 concentrations in seven microenvironments. A probabilistic approach was used to develop the Korea simulation exposure model. The determinants for the population exposure group were identified using a multinomial logistic regression analysis. Population exposure to PM_2.5 varied significantly among the three seasons (p < 0.01). The mean ± standard deviation of population exposures to PM_2.5 was 21.3 ± 4.0 μg/m³ in summer, 9.8 ± 2.7 μg/m³ in autumn, and 29.9 ± 10.6 μg/m³ in winter. Exposure to PM_2.5 higher than 35 μg/m³ mainly occurred in winter. Gender, age, working hours, and health condition were identified as significant determinants in the exposure groups. An "unhealthy" health condition was the most significant determinant. The high PM_2.5 exposure group was characterized as a higher proportion of males of a lower age with longer working hours. The population exposure model for PM_2.5 could be used to implement effective interventions and evaluate the effectiveness of control policies to reduce exposure.

Commuters' Personal Exposure Assessment and Evaluation of Inhaled Dose to Different Atmospheric Pollutants

Several studies evaluating exposure to pollutants in microenvironments (MEs) are available in the scientific literature, but studies that evaluate the inhaled doses of pollutants are few in number. Therefore, this study aimed to evaluate the exposure of commuters to different pollutants (i.e., nitrogen dioxide [NO₂] and fractionated particulate matter [PM], including ultrafine particles [UFPs]) using miniaturized and portable real-time monitoring instruments in selected MEs; the inhaled doses of these pollutants were estimated for each of these MEs. Measurements were performed along a typical commute, considering different traffic and nontraffic MEs. Experimental data were collected over four working weeks in two different seasons (winter and summer). Different portable and miniaturized instruments were used to evaluate PM and NO₂ exposure. Furthermore, physiological parameters were evaluated using a heart rate monitor. The principal results show that higher exposure levels were measured in Underground (for all PM fractions and NO₂) and in Car (UFP), while lower levels were measured in Car (PM and NO₂) and in Train (UFP). In contrast, higher values of the inhaled cumulative dose were estimated in environments defined as Other, followed by Walking (ht), while lower values were observed in Walking (lt) and in Car.

PM2.5 and NO2 exposure errors using proxy measures, including derived personal exposure from outdoor sources: A systematic review and meta-analysis

BACKGROUND

The use of proxy exposure estimates for PM_2.5 and NO₂ in air pollution studies instead of personal exposures, introduces measurement error, which can produce biased epidemiological effect estimates. Most studies consider total personal exposure as the gold standard. However, when studying the effects of ambient air pollution, personal exposure from outdoor sources is the exposure of interest.

OBJECTIVES

We assessed the magnitude and variability of exposure measurement error by conducting a systematic review of the differences between personal exposures from outdoor sources and the corresponding measurements for ambient concentrations in order to increase understanding of the measurement error structures of the pollutants.

DATA SOURCES AND ELIGIBILITY CRITERIA

We reviewed the literature (ISI Web of Science, Medline, 2000-2016) for English language studies (in any age group in any location (NO₂) or Europe and North America (PM_2.5)) that reported repeated measurements over time both for personal and ambient PM_2.5 or NO₂ concentrations. Only a few studies reported personal exposure from outdoor sources. We also collected data for infiltration factors and time-activity patterns of the individuals in order to estimate personal exposures from outdoor sources in every study.

STUDY APPRAISAL AND SYNTHESIS METHODS

Studies using modelled rather than monitored exposures were excluded. Type of personal exposure monitor was assessed. Random effects meta-analysis was conducted to quantify exposure error as the mean difference between "true" and proxy measures.

RESULTS

Thirty-two papers for PM_2.5 and 24 for NO₂ were identified. Outdoor sources were found to contribute 44% (range: 33-55%) of total personal exposure to PM_2.5 and 74% (range: 57-88%) to NO₂. Overall estimates of personal exposure (24-hour averages) from outdoor sources were 9.3 μg/m³ and 12.0 ppb for PM_2.5 and NO₂ respectively, while the corresponding difference between these exposures and the ambient concentrations (i.e. the measurement error) was 5.72 μg/m³ and 7.17 ppb. Our findings indicated also higher error variability for NO₂ than PM_2.5. Large heterogeneity was observed which was not explained sufficiently by geographical location or age group of the study sample.

LIMITATIONS, CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

Relying only on information available in published studies led to some limitations: the contribution of outdoor sources to total personal exposure for NO₂ had to be inferred, individual variation in exposure misclassification was unavailable and instrument error could not be addressed. The larger magnitude and variability of errors for NO₂ compared with PM_2.5 has implications for biases in the health effect estimates of multi-pollutant epidemiological models. Results suggest that further research is needed regarding personal exposure studies and measurement error bias in epidemiological models.

Modeling of residential indoor PM2.5 exposure in 37 counties in China

It is critical to estimate the exposure to indoor air pollution of residents spending most of their time in such microenvironments. However, the understanding regarding PM_2.5 exposure in residential indoor environments is very limited. In this study, we collected participants' basic information and time-activity patterns, as well as details of other factors related to indoor air pollution exposure, through questionnaires presented to a large population in 37 counties of China. Continuous monitoring of ambient PM_2.5 concentrations was performed using an environmental fixed-site monitoring network. Residential indoor PM_2.5 concentrations were predicted using a mass balance model based on the data obtained. Evaluation of continuous daily average residential indoor PM_2.5 exposure doses for large populations during winter revealed concentrations ranged from 67 to 195 μg/m³. Finally, differences in residential indoor PM_2.5 exposure between northern and southern China were investigated. The results suggested that residential indoor PM_2.5 concentrations in northern China, associated with heating, were higher than in the south. The established model could be important for improved understanding of human exposure to indoor PM_2.5 air pollution.

A multipollutant evaluation of APEX using microenvironmental ozone, carbon monoxide, and particulate matter (PM2.5) concentrations measured in Los Angeles by the exposure classification project

This paper describes an operational evaluation of the US Environmental Protection Agency's (EPA) Air Pollution Exposure Model (APEX). APEX simulations for a multipollutant ambient air mixture, i.e. ozone (O₃), carbon monoxide (CO), and particulate matter 2.5 microns in diameter or less (PM_2.5), were performed for two seasons in three study areas in central Los Angeles. APEX predicted microenvironmental concentrations were compared with concentrations of these three pollutants monitored in the Exposure Classification Project (ECP) study during the same periods. The ECP was designed expressly for evaluating exposure models and measured concentrations inside and outside 40 microenvironments. This evaluation study identifies important uncertainties in APEX inputs and model predictions useful for guiding further exposure model input data and algorithm development efforts. This paper also presents summaries of the concentrations in the different microenvironments.

PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

The microenvironmental modelling approach to assess children's exposure to air pollution - A review

Exposures to a wide spectrum of air pollutants were associated to several effects on children's health. Exposure assessment can be used to establish where and how air pollutants' exposures occur. However, a realistic estimation of children's exposures to air pollution is usually a great ethics challenge, especially for young children, because they cannot intentionally be exposed to contaminants and according to Helsinki declaration, they are not old enough to make a decision on their participation. Additionally, using adult surrogates introduces bias, since time-space-activity patterns are different from those of children. From all the different available approaches for exposure assessment, the microenvironmental (ME) modelling (indirect approach, where personal exposures are estimated or predicted from microenvironment measurements combined with time-activity data) seemed to be the best to assess children's exposure to air pollution as it takes into account the varying levels of pollution to which an individual is exposed during the course of the day, it is faster and less expensive. Thus, this review aimed to explore the use of the ME modelling approach methodology to assess children's exposure to air pollution. To meet this goal, a total of 152 articles, published since 2002, were identified and titles and abstracts were scanned for relevance. After exclusions, 26 articles were fully reviewed and main characteristics were detailed, namely: (i) study design and outcomes, including location, study population, calendar time, pollutants analysed and purpose; and (ii) data collection, including time-activity patterns (methods of collection, record time and key elements) and pollution measurements (microenvironments, methods of collection and duration and time resolution). The reviewed studies were from different parts of the world, confirming the worldwide application, and mostly cross-sectional. Longitudinal studies were also found enhancing the applicability of this approach. The application of this methodology on children is different from that on adults because of data collection, namely the methods used for collecting time-activity patterns must be different and the time-activity patterns are itself different, which leads to select different microenvironments to the data collection of pollutants' concentrations. The most used methods to gather information on time-activity patterns were questionnaires and diaries, and the main microenvironments considered were home and school (indoors and outdoors). Although the ME modelling approach in studies to assess children's exposure to air pollution is highly encouraged, a validation process is needed, due to the uncertainties associated with the application of this approach.

Modeling population exposure to ultrafine particles in a major Italian urban area

Average daily ultrafine particles (UFP) exposure of adult Milan subpopulations (defined on the basis of gender, and then for age, employment or educational status), in different exposure scenarios (typical working day in summer and winter) were simulated using a microenvironmental stochastic simulation model. The basic concept of this kind of model is that time-weighted average exposure is defined as the sum of partial microenvironmental exposures, which are determined by the product of UFP concentration and time spent in each microenvironment. In this work, environmental concentrations were derived from previous experimental studies that were based on microenvironmental measurements in the city of Milan by means of personal or individual monitoring, while time-activity patterns were derived from the EXPOLIS study. A significant difference was observed between the exposures experienced in winter (W: 28,415 pt/cm3) and summer (S: 19,558 pt/cm3). Furthermore, simulations showed a moderate difference between the total exposures experienced by women (S: 19,363 pt/cm3; W: 27,623 pt/cm3) and men (S: 18,806 pt/cm3; W: 27,897 pt/cm3). In addition, differences were found as a function of (I) age, (II) employment status and (III) educational level; accordingly, the highest total exposures resulted for (I) 55-59 years old people, (II) housewives and students and (III) people with higher educational level (more than 10 years of scholarity). Finally, significant differences were found between microenvironment-specific exposures.

Assessment of interindividual and geographic variability in human exposure to fine particulate matter in environmental tobacco smoke

Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures to fine particulate matter of 2.5 μm or smaller (PM(2.5) ). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) Model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM(2.5) exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS-PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass-balance approach, which is comparable to best practices. The default inputs in SHEDS-PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the proportion of smokers and "other smokers" and cigarette emission rate were found to be important. SHEDS-PM does not currently account for in-vehicle ETS exposure; however, in-vehicle ETS-related PM(2.5) levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass-balance-based methodology for estimating in-vehicle ETS PM(2.5) concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS-PM model. Interindividual variability for ETS exposure was quantified. Geographic variability in ETS exposure was quantified based on the varying prevalence of smokers in five selected locations in the United States.

Modelling inhalation exposure to combustion-related air pollutants in residential buildings: Application to health impact assessment

Buildings in developed countries are becoming increasingly airtight as a response to stricter energy efficiency requirements. At the same time, changes are occurring to the ways in which household energy is supplied, distributed and used. These changes are having important impacts on exposure to indoor air pollutants in residential buildings and present new challenges for professionals interested in assessing the effects of housing on public health. In many circumstances, models are the most appropriate way with which to examine the potential outcomes of future environmental and/or building interventions and policies. As such, there is a need to consider the current state of indoor air pollution exposure modelling. Various indoor exposure modelling techniques are available, ranging from simple statistical regression and mass-balance approaches, to more complex multizone and computational fluid dynamics tools that have correspondingly large input data requirements. This review demonstrates that there remain challenges which limit the applicability of current models to health impact assessment. However, these issues also present opportunities for better integration of indoor exposure modelling and epidemiology in the future. The final part of the review describes the application of indoor exposure models to health impact assessments, given current knowledge and data, and makes recommendations aimed at improving model predictions in the future.

Contribution to volatile organic compound exposures from time spent in stores and restaurants and bars

Many people spend time in stores and restaurants, yet there has been little investigation of the influence of these microenvironments on personal exposure. Relative to the outdoors, transportation, and the home, these microenvironments have high concentrations of several volatile organic compounds (VOCs). We developed a stochastic model to examine the effect of VOC concentrations in these microenvironments on total personal exposure for (1) non-smoking adults working in offices who spend time in stores and restaurants or bars and (2) non-smoking adults who work in these establishments. We also compared the effect of working in a smoking versus non-smoking restaurant or bar. Input concentrations for each microenvironment were developed from the literature whereas time activity inputs were taken from the National Human Activity Patterns Survey. Time-averaged exposures were simulated for 5000 individuals over a weeklong period for each analysis. Mean contributions to personal exposure from non-working time spent in stores and restaurants or bars range from <5% to 20%, depending on the VOC and time-activity patterns. At the 95th percentile of the distribution of the proportion of personal exposure attributable to time spent in stores and restaurants or bars, these microenvironments can be responsible for over half of a person's total exposure to certain VOCs. People working in restaurants or bars where smoking is allowed had the highest fraction of exposure attributable to their workplace. At the median, people who worked in stores or restaurants tended to have 20-60% of their total exposures from time spent at work. These results indicate that stores and restaurants can be large contributors to personal exposure to VOCs for both workers in those establishments and for a subset of people who visit these places, and that incorporation of these non-residential microenvironments can improve models of personal exposure distributions.

Spatial variations in estimated chronic exposure to traffic-related air pollution in working populations: a simulation

Background

Chronic exposure to traffic-related air pollution is associated with a variety of health impacts in adults and recent studies show that exposure varies spatially, with some residents in a community more exposed than others. A spatial exposure simulation model (SESM) which incorporates six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work and in-vehicle other) is described and used to explore spatial variability in estimates of exposure to traffic-related nitrogen dioxide (not including indoor sources) for working people. The study models spatial variability in estimated exposure aggregated at the census tracts level for 382 census tracts in the Greater Vancouver Regional District of British Columbia, Canada. Summary statistics relating to the distributions of the estimated exposures are compared visually through mapping. Observed variations are explored through analyses of model inputs.

Results

Two sources of spatial variability in exposure to traffic-related nitrogen dioxide were identified. Median estimates of total exposure ranged from 8 μg/m³ to 35 μg/m³ of annual average hourly NO₂ for workers in different census tracts in the study area. Exposure estimates are highest where ambient pollution levels are highest. This reflects the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. However, for workers within the same census tract, variations were observed in the partial exposure estimates associated with time spent outside the residential census tract. Simulation modeling shows that some workers may have exposures 1.3 times higher than other workers residing in the same census tract because of time spent away from the residential census tract, and that time spent in work census tracts contributes most to the differences in exposure. Exposure estimates associated with the activity of commuting by vehicle to work were negligible, based on the relatively short amount of time spent in this microenvironment compared to other locations. We recognize that this may not be the case for pollutants other than NO_2. These results represent the first time spatially disaggregated variations in exposure to traffic-related air pollution within a community have been estimated and reported.

Conclusion

The results suggest that while time spent in the home indoor microenvironment contributes most to between-census tract variation in estimates of annual average exposures to traffic-related NO₂, time spent in the work indoor microenvironment contributes most to within-census tract variation, and time spent in transit by vehicle makes a negligible contribution. The SESM has potential as a policy evaluation tool, given input data that reflect changes in pollution levels or work flow patterns due to traffic demand management and land use development policy.

Characterisation of urban inhalation exposures to benzene, formaldehyde and acetaldehyde in the European Union: comparison of measured and modelled exposure data

BACKGROUND, AIM AND SCOPE

All across Europe, people live and work in indoor environments. On average, people spend around 90% of their time indoors (homes, workplaces, cars and public transport means, etc.) and are exposed to a complex mixture of pollutants at concentration levels that are often several times higher than outdoors. These pollutants are emitted by different sources indoors and outdoors and include volatile organic compounds (VOCs), carbonyls (aldehydes and ketones) and other chemical substances often adsorbed on particles. Moreover, legal obligations opposed by legislations, such as the European Union's General Product Safety Directive (GPSD) and Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), increasingly require detailed understanding of where and how chemical substances are used throughout their life-cycle and require better characterisation of their emissions and exposure. This information is essential to be able to control emissions from sources aiming at a reduction of adverse health effects. Scientifically sound human risk assessment procedures based on qualitative and quantitative human exposure information allows a better characterisation of population exposures to chemical substances. In this context, the current paper compares inhalation exposures to three health-based EU priority substances, i.e. benzene, formaldehyde and acetaldehyde.

MATERIALS AND METHODS

Distributions of urban population inhalation exposures, indoor and outdoor concentrations were created on the basis of measured AIRMEX data in 12 European cities and compared to results from existing European population exposure studies published within the scientific literature. By pooling all EU city personal exposure, indoor and outdoor concentration means, representative EU city cumulative frequency distributions were created. Population exposures were modelled with a microenvironment model using the time spent and concentrations in four microenvironments, i.e. indoors at home and at work, outdoors at work and in transit, as input parameters. Pooled EU city inhalation exposures were compared to modelled population exposures. The contributions of these microenvironments to the total daily inhalation exposure of formaldehyde, benzene and acetaldehyde were estimated. Inhalation exposures were compared to the EU annual ambient benzene air quality guideline (5 microg/m3-to be met by 2010) and the recommended (based on the INDEX project) 30-min average formaldehyde limit value (30 microg/m3).

RESULTS

Indoor inhalation exposure contributions are much higher compared to the outdoor or in-transit microenvironment contributions, accounting for almost 99% in the case of formaldehyde. The highest in-transit exposure contribution was found for benzene; 29.4% of the total inhalation exposure contribution. Comparing the pooled AIRMEX EU city inhalation exposures with the modelled exposures, benzene, formaldehyde and acetaldehyde exposures are 5.1, 17.3 and 11.8 microg/m3 vs. 5.1, 20.1 and 10.2 microg/m3, respectively. Together with the fact that a dominating fraction of time is spent indoors (>90%), the total inhalation exposure is mostly driven by the time spent indoors.

DISCUSSION

The approach used in this paper faced three challenges concerning exposure and time-activity data, comparability and scarce or missing in-transit data inducing careful interpretation of the results. The results obtained by AIRMEX underline that many European urban populations are still exposed to elevated levels of benzene and formaldehyde in the inhaled air. It is still likely that the annual ambient benzene air quality guideline of 5 microg/m3 in the EU and recommended formaldehyde 30-min average limit value of 30 microg/m3 are exceeded by a substantial part of populations living in urban areas. Considering multimedia and multi-pathway exposure to acetaldehyde, the biggest exposure contribution was found to be related to dietary behaviour rather than to inhalation.

CONCLUSIONS

In the present study, inhalation exposures of urban populations were assessed on the basis of novel and existing exposure data. The indoor residential microenvironment contributed most to the total daily urban population inhalation exposure. The results presented in this paper suggest that a significant part of the populations living in European cities exceed the annual ambient benzene air quality guideline of 5 microg/m3 in the EU and recommended (INDEX project) formaldehyde 30-min average limit value of 30 microg/m3.

RECOMMENDATIONS AND PERSPECTIVES

To reduce exposures and consequent health effects, adequate measures must be taken to diminish emissions from sources such as materials and products that especially emit benzene and formaldehyde in indoor air. In parallel, measures can be taken aiming at reducing the outdoor pollution contribution indoors. Besides emission reduction, mechanisms to effectively monitor and manage the indoor air quality should be established. These mechanisms could be developed by setting up appropriate EU indoor air guidelines.

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

BACKGROUND

The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles.

METHODS

Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties.

RESULTS

The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties.

CONCLUSION

When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.

Physico-chemical characterization of indoor/outdoor particulate matter in two residential houses in Oslo, Norway: measurements overview and physical properties--URBAN-AEROSOL Project

Indoor/outdoor measurements have been performed in the Oslo metropolitan area during summer and winter periods (2002-2003) at two different residential houses. The objective of the measurement study was to characterize, physically and chemically, the particulate matter (PM) and gaseous pollutants associated with actual human exposure in the selected places, and their indoor/outdoor relationship. In this paper, we focus on the PM measurements and examine the relationship between the indoor and outdoor PM concentrations taking into account the ventilation rate, indoor sources and meteorological conditions. The indoor/outdoor measurements indicate the important contribution of the outdoor air to the indoor air quality and the influence of specific indoor sources such as smoking and cooking to the concentration of PM inside houses. However, no specific correlation was found between the indoor/outdoor concentration ratio and the meteorological parameters. This study provides information on the physical characteristics and the relationship of indoor to outdoor concentration of particulate matter in residential houses. Moreover, the parameters that influence this relationship are discussed. The results presented here are specific to the sampled houses and conditions used and provide data on the actual human exposure characteristics which occur in the spatial and temporal scales of the present study.

Reduction potential of urban PM2.5 mortality risk using modern ventilation systems in buildings

Urban PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 microm) is associated with excess mortality and other health effects. Stationary sources are regulated and considerable effort is being put into developing low-pollution vehicles and environment-friendly transportation systems. While waiting for technological breakthroughs in emission controls, the current work assesses the exposure reductions achievable by a complementary means: efficient filtration of supply air in buildings. For this purpose infiltration factors for buildings of different ages are quantified using Exposures of Adult Urban Populations in Europe Study (EXPOLIS) measurements of indoor and outdoor concentrations in a population-based probability sample of residential and occupational buildings in Helsinki, Finland. These are entered as inputs into an evaluated simulation model to compare exposures in the current scenario with an alternative scenario, where the distribution of ambient PM2.5 infiltration factors in all residential and occupational buildings are assumed to be similar to the subset of existing occupational buildings using supply air filters. In the alternative scenario exposures to ambient PM2.5 were reduced by 27%. Compared with source controls, a significant additional benefit is that infiltration affects particles from all outdoor sources. The large fraction of time spent indoors makes the reduction larger than what probably can be achieved by local transport policies or other emission controls in the near future.

PRACTICAL IMPLICATIONS

It has been suggested that indoor concentrations of ambient particles and the associated health risks can be reduced by using mechanical ventilation systems with supply air filtering in buildings. The current work quantifies the effects of these concentration reductions on population exposures using population-based data from Helsinki and an exposure model. The estimated exposure reductions suggest that correctly defined building codes may reduce annual premature mortality by hundreds in Finland and by tens of thousands in the developed world altogether.

The EXPOLIS study: implications for exposure research and environmental policy in Europe

Exposure analysis is a crucial part of effective management of public health risks caused by pollutants and chemicals in our environment. During the last decades, more data required for exposure analysis has become available, but the need for direct population based measurements of exposures is still clear. The current work (i) describes the European EXPOLIS study, designed to produce this kind of exposure data for major air pollutants in Europe, and the database created to make the collected data available for researchers (ii) reviews the exposure analysis conducted and results published so far using these data and (iii) discusses the implications of the results from the point of view of research and environmental policy in Europe. Fine particle (with 37 elements and black smoke), nitrogen dioxide, volatile organic compounds (30 compounds) and carbon monoxide inhalation exposures and exposure-related questionnaire data were measured in seven European cities during 1996-2000. The EXPOLIS database has been used for exposure analysis of these pollutants for 4 years now and results have been published in approximately 30 peer-reviewed journal papers, demonstrating the versatility, usability and scientific value of such a data set. The multipollutant exposure data from the same subjects in the random population samples allows for analyses of the determinants, microenvironments and sources of exposures to multipollutant mixtures and associations between the different air pollutants. This information is necessary and useful for developing effective policies and control strategies for healthier environment.

Simulation of working population exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data

Current air pollution levels have been shown to affect human health. Probabilistic modeling can be used to assess exposure distributions in selected target populations. Modeling can and should be used to compare exposures in alternative future scenarios to guide society development. Such models, however, must first be validated using existing data for a past situation. This study applied probabilistic modeling to carbon monoxide (CO) exposures using EXPOLIS-Milan data. In the current work, the model performance was evaluated by comparing modeled exposure distributions to observed ones. Model performance was studied in detail in two dimensions; (i) for different averaging times (1, 8 and 24 h) and (ii) using different detail in defining the microenvironments in the model (two, five and 11 microenvironments). (iii) The number of exposure events leading to exceeding the 8-h guideline was estimated. Population time activity was modeled using a fractions-of-time approach assuming that some time is spent in each microenvironment used in the model. This approach is best suited for averaging times from 24 h upwards. In this study, we tested how this approach affects results when used for shorter averaging times, 1 and 8 h. Models for each averaging time were run with two, five and 11 microenvironments. The two-microenvironment models underestimated the means and standard deviations (SDs) slightly for all averaging times. The five- and 11-microenvironment models matched the means quite well but underestimated SDs in several cases. For 1- and 24-h averaging times the simulated SDs are slightly smaller than the corresponding observed values. The 8-h model matched the observed exposure levels best. The results show that for CO (i) the modeling approach can be applied for averaging times from 8 to 24 h and as a screening model even to an averaging time of 1 h; (ii) the number of microenvironments affects only weakly the results and in the studied cases only exposure levels below the 80th percentile; (iii) this kind of model can be used to estimate the number of high-exposure events related to adverse health effects. By extrapolation beyond the observed data, it was shown that Milanese office workers may experience adverse health effects caused by CO.

Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter

As a part of the EXPOLIS study, a stochastic exposure-modeling framework was developed. The framework is useful to compare exposure distributions of different (sub-) populations or different scenarios, and to gain insight into population exposure distributions and exposure determinants. It was implemented in an MS-Excel workbook using @Risk add-on software. Basic concept of the framework is that time-weighted average exposure is a sum of partial exposures in the visited microenvironments. Partial exposure is determined by the concentration and the time spent in the microenvironment. In the absence of data, indoor concentrations are derived as a function of ambient concentrations, effective penetration rates and contribution of indoor sources. Framework input parameters are described by probability distributions. A lognormal distribution is assumed for the microenvironment concentrations and for the contribution of indoor sources, and a beta distribution for the time spent in a microenvironment and for the penetration factor. Mean and standard deviation values parameterize the distributions. In this paper, Latin Hypercube sampling is used for the input distributions. The outcome of the framework is an estimate of the population exposure distribution for the selected air pollutant. The framework is best suited for averaging times from 24 h upwards. Sensitivity analyses can be performed to determine the most influential factors of exposure. The application of the framework is illustrated in two examples. The EXPOLIS PM(2.5) example uses microenvironment measurement and time-activity data from the EXPOLIS study to model PM(2.5) population exposure distributions in four European cities. The results are compared to the observed personal exposure distributions from the same study. The Dutch PM(10) example uses input data from several (Dutch) databases and from literature, and shows a more complex application of the framework for comparison of scenarios and subpopulations.